Applying our machine learning system to the quarterbacks in the 2016 NFL draft, the worst values are as follows. The system ranks quarterbacks as yes or no draft and assigns a probability based only on the players college statistics.

1. Jets – Christian Hackenberg – no (73%)
New York Jest took Christian Hackenberg as the 4th quarterback in the 2016 draft. Our machine learning QB system rated him a “no” with an 73% probability.

2. Patriots – Jacoby Brissett – no (98%)
New England selected Jacoby Brissett as the 5th quarterback in the draft. Brissett had our systems highest no ranking at 98%. We suspect he may be utilized in other than a QB capacity. We will see…

3. Raiders – Connor Cook – no (84%)
Oakland drafted Connor Cook as the 7th QB taken yet he was a “no” draft with an 84% ranking.

Here are some more numbers to consider from our system:

Drafted only 23 of 32 starting QBs for 2015
12 playoff teams in 2015, the system drafted 10 QBs (83%)
2015 Divisional Round the system drafted 7 of 8 QBs (87%)
2015 Conference Finals, the system drafted all 4 QBs (100%)
Super Bowl 50 teams, the system drafted both QBs. (100%)
Drafted every 2015 division winning quarterback except one – Hoyer. (87%)

Applying our machine learning system to the quarterbacks in the 2016 NFL draft, the best values are as follows. The system ranks quarterbacks as yes or no draft and assigns a probability based only on the players college statistics.

1. Dolphins – Brandon Doughtey – yes (86%)
Miami took Brandon Doughtey as the last quarterback in the 2016 draft. Our machine learning QB system rated him a “yes” with an 86% probability.

2. Bills – Cardale Jones – yes (97%)
Buffalo selected Cardale Jones as the 9th quarterback in the draft. Jones had our systems highest yes ranking at 97%.

3. Chiefs – Kevin Hogan – yes (82%)
Kansas City drafted Kevin Hogan as the 10th QB taken yet he was a yes draft with an 82% ranking.

4. Lions – Jake Ruddock – yes (83%)
Detroit took Michigan QB Jake Ruddock as the 12th quarterback in the draft. Our system had Ruddock as a yes with an 83% prorbability.

Here are some more numbers to consider from our system:

Drafted only 23 of 32 starting QBs for 2015
12 playoff teams in 2015, the system drafted 10 QBs (83%)
2015 Divisional Round the system drafted 7 of 8 QBs (87%)
2015 Conference Finals, the system drafted all 4 QBs (100%)
Super Bowl 50 teams, the system drafted both QBs. (100%)
Drafted every 2015 division winning quarterback except one – Hoyer. (87%)

The following is a list of the 2016 Quarterbacks drafted in the order they were taken. After the name of the team that took them, we compare the draft picks with out Machine Learning QB analysis system to see what teams took “yes” players and which teams took “no” players. The players are rated yes or no if they would have been drafted and the probability the system thinks they fit in that category.

Jared Goff – Los Angeles – yes – (89%)
Carson Wentz – Philadelphia – yes – (83%)
Paxton Lynch – Denver – yes – (95%)
Christian Hackenberg – NY Jets – no – (73%)
Jacoby Brissett – New England – no – (98%)
Cody Kessler – Cleveland – yes – (90%)
Connor Cook – Oakland – no – (84%)
Dak Prescott – Dallas – yes – (56%)
Cardale Jones – Buffalo – yes – (97%)
Kevin Hogan – Kansas City – yes (82%)
Nate Sudfeld – Washington – no – (81%)
Jake Rudock – Detroit – yes – (83%)
Brandon Allen – Jacksonville – no – (95%)
Jeff Driskel – San Francisco – yes – (60%)
Brandon Doughty – Miami – yes – (86%)

As we watch the class of 2016 quarterbacks progress, it will be interesting to look back at this list and see where they stood at the day of the draft according to our Machine Learning QB analysis system

Machine Learning and big data

Machine learning is a fundamental tool in creating a world that can sense and react to dynamic, distributed phenomena. The number of variables and factors that can be taken into consideration by this methodology is unlimited.

It weaves together real-time data collection with the automation of business processes, and is ideally suited to deal with complex, disparate data sources and the high number of variables involved in data sets that are large, diverse and fast changing. In other words, machine learning is primed to handle big data.

Wherein traditional analytics tools are limited by data volume and the need for human interaction to specify program execution, machine learning offers the scale, speed and accuracy needed to truly uncover the full value of big data.

– See more at: http://www.information-age.com/it-management/strategy-and-innovation/123460772/machine-learning-set-unlock-power-big-data#sthash.2LEaz6Pz.dpuf

A recent query using Google Trends shows an interesting level of interest in machine learning over time (see figure below). There was an emergence in hype around the 2005 time-frame and led to a cooling off period, but once big data started heating up around 2010, the upward swing in interest continues until today. The good thing is that “machine learning” really is just a confluence of related disciplines like computer science, statistics and mathematics. These fields aren’t going anywhere and neither is machine learning. Statistical learning is here to stay!

Machine Learning Trending on Google